[1]} used an adversarial training approach for the same purpose. They added noise to the word embeddings to make the model more robust for distantly supervised training. [2]} used a generative adversarial network (GAN) to address the issue of noisy samples in relation extraction. They used a separate binary classifier as a generator in their model for each positive relation class to identify the true positives for that relation and filter out the noisy ones. [3]} used reinforcement learning to identify the noisy samples for the positive relation classes. [4]} used reinforcement learning to identify the noisy samples for the positive relations and then used the identified noisy samples as unlabeled data in their model. [5]} used a clustering approach to identify the noisy samples. They assigned the correct relation label to these noisy samples and used them as additional training data in their model.
